def 							UpperCAmelCase		(       lowercase = "The quick brown fox jumps over the lazy dog"				,     ):







				"""simple docstring"""



				__lowercase					     =				set()

				# Replace all the whitespace in our sentence
				__lowercase					     =				input_str.replace(''' '''				,     ''''''				)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()				)
				return len(lowercase				) == 26





def 							UpperCAmelCase		(       lowercase = "The quick brown fox jumps over the lazy dog"				,     ):







				"""simple docstring"""



				__lowercase					     =				[False] * 26
				for char in input_str:
								if char.islower():
												__lowercase					     =				True
								elif char.isupper():
												__lowercase					     =				True
				return all(lowercase				)





def 							UpperCAmelCase		(       lowercase = "The quick brown fox jumps over the lazy dog"				,     ):







				"""simple docstring"""



				return len({char for char in input_str.lower() if char.isalpha()}				) == 26





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				from timeit import timeit

				__lowercase					     =				'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
				print(timeit('''is_pangram()'''				,     setup=lowercase				)				)
				print(timeit('''is_pangram_faster()'''				,     setup=lowercase				)				)
				print(timeit('''is_pangram_fastest()'''				,     setup=lowercase				)				)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)